Report last updated Mon May 23 19:08:37 2016.

Exploratory analysis

In this section we will see descriptive figures about quality of the data, reads with adapter, reads mapped to miRNAs, reads mapped to other small RNAs.

size distribution

After adapter removal, we can plot the size distribution of the small RNAs.

miRNA

total miRNA expression annotated with mirbase

Distribution of mirna expression

cumulative distribution of miRNAs

Clustering

MDS plot

complexity

Number of miRNAs with > 3 counts.

colSums(counts > 10)
A201_Day0 348
A200_Day0 380
A196_Day0 363
A190_Day28 389
A189_Day14 374
A181_Day7 380
A180_Day7 420
A178_Day3 399
A172_Day2 387
A167_Day1 338
A165_Day1 369
A89_Day28 365
A86_Day28 376
A85_Day14 355
A84_Day14 394
A80_Day7 392
A75_Day3 373
A74_Day3 409
A70_Day2 430
A69_Day2 423
A63_Day1 372

novel miRNA by mirdeep2

total miRNA expression

Distribution of mirna expression

cumulative distribution of miRNAs

Clustering

MDS plot

complexity

Number of miRNAs with > 3 counts.

colSums(counts > 10)
A201_Day0 75
A200_Day0 93
A196_Day0 82
A190_Day28 87
A189_Day14 80
A181_Day7 88
A180_Day7 103
A178_Day3 91
A172_Day2 90
A167_Day1 71
A165_Day1 86
A89_Day28 76
A86_Day28 85
A85_Day14 73
A84_Day14 86
A80_Day7 79
A75_Day3 78
A74_Day3 94
A70_Day2 107
A69_Day2 102
A63_Day1 87

Others small RNA

The data was analyzed with seqcluster

This tools used all reads, uniquely mapped and multi-mapped reads. The first step is to cluster sequences in all locations they overlap. The second step is to create meta-clusters: is the unit that merge all clusters that share the same sequences. This way the output are meta-clusters, common sequences that could come from different region of the genome.

genome covered

In this table 1 means % of the genome with at least 1 read, and 0 means % of the genome without reads.

The normal value for human data with strong small RNA signal is: 0.0002. This will change for smaller genomes.

classification

Number of reads in the data after each step:

  • raw: initial reads
  • cluster: after cluster detection
  • multimap: after meta-cluster detection

Check complex meta-clusters: This kind of events happen when there are small RNA over the whole genome, and all repetitive small rnas map to thousands of places and sharing many sequences in many positions. If any meta-cluster is > 40% of the total data, maybe it is worth to add some filters like: minimum number of counts -e or --min--shared in seqcluster prepare

     A201_Day0 A200_Day0 A196_Day0 A190_Day28 A189_Day14 A181_Day7
     A180_Day7 A178_Day3 A172_Day2 A167_Day1 A165_Day1 A89_Day28 A86_Day28
     A85_Day14 A84_Day14 A80_Day7 A75_Day3 A74_Day3 A70_Day2 A69_Day2
     A63_Day1

complexity

Number of miRNAs with > 10 counts.

colSums(clus_ma > 10)
A201_Day0 536
A200_Day0 545
A196_Day0 559
A190_Day28 556
A189_Day14 555
A181_Day7 554
A180_Day7 564
A178_Day3 574
A172_Day2 546
A167_Day1 547
A165_Day1 577
A89_Day28 536
A86_Day28 561
A85_Day14 578
A84_Day14 563
A80_Day7 553
A75_Day3 564
A74_Day3 550
A70_Day2 562
A69_Day2 563
A63_Day1 559

Contribution by class

Differential expression

DESeq2 is used for this analysis.

Analysis for miRNA

## Comparison: fa_model_mirna {.tabset}


out of 645 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 163, 25%
LFC < 0 (down) : 133, 21%
outliers [1] : 1, 0.16%
low counts [2] : 88, 14%
(mean count < 1)
[1] see ‘cooksCutoff’ argument of ?results
[2] see ‘independentFiltering’ argument of ?results

NULL

Differential expression file at: fa_model_mirna.tsv

Normalized counts matrix file at: fa_model_mirna_log2_counts.tsv

MA plot plot

Volcano plot

QC for DE genes p-values/variance

Most significand, FDR< 0.05 and log2FC > 1 : 119

Plots most significand

Plot top 9 genes

Top DE genes

baseMean log2FoldChange lfcSE stat pvalue padj symbol description Day14vsDay0 Day1vsDay0 Day2vsDay0 Day3vsDay0 Day7vsDay0 absMaxLog2FC
mmu-miR-21a-3p 3349.90195 0.2092341 0.2123191 373.2788 0 0 mmu-miR-21a-3p mmu-miR-21a-3p 1.2023999 2.6461098 3.1643137 2.7118794 2.2483118 3.1643137
mmu-miR-21a-5p 235757.69370 0.5885233 0.1674390 323.3068 0 0 mmu-miR-21a-5p mmu-miR-21a-5p 1.5993843 2.0354839 2.4120192 2.3303773 2.3425021 2.4120192
mmu-miR-92a-1-5p 139.77162 -0.1588406 0.1761096 293.8874 0 0 mmu-miR-92a-1-5p mmu-miR-92a-1-5p 0.3916372 1.5100673 1.6814937 1.2625307 0.8592778 1.6814937
mmu-miR-181c-5p 5177.32928 0.7730872 0.1141849 270.6441 0 0 mmu-miR-181c-5p mmu-miR-181c-5p 0.6225336 -0.4106138 -0.4550042 -0.5910839 0.1610376 0.6225336
mmu-miR-375-3p 1831.43198 0.6415690 0.1500521 249.5976 0 0 mmu-miR-375-3p mmu-miR-375-3p 1.5341903 0.2266603 1.1013174 1.5199590 1.7979345 1.7979345
mmu-miR-130b-3p 121.24753 0.9038785 0.2313726 235.2099 0 0 mmu-miR-130b-3p mmu-miR-130b-3p 1.8024114 0.7083430 1.6076363 2.4460588 2.4122506 2.4460588
mmu-miR-199a-5p 1494.91668 1.1819872 0.1614447 225.4967 0 0 mmu-miR-199a-5p mmu-miR-199a-5p 1.7951627 0.2449684 0.5223752 1.0193132 1.7315863 1.7951627
mmu-miR-199a-3p 15543.36157 1.1521346 0.1641965 216.1439 0 0 mmu-miR-199a-3p mmu-miR-199a-3p 1.6577361 0.0145188 0.1361008 0.6047284 1.4022824 1.6577361
mmu-miR-199b-3p 15532.96146 1.1525354 0.1642081 216.0259 0 0 mmu-miR-199b-3p mmu-miR-199b-3p 1.6577379 0.0150815 0.1362317 0.6042545 1.4016804 1.6577379
mmu-miR-146b-5p 1879.08619 1.6373420 0.2285428 211.7384 0 0 mmu-miR-146b-5p mmu-miR-146b-5p 2.8223531 1.1898393 1.5489137 2.6665250 2.7112640 2.8223531
mmu-miR-181a-5p 79471.80435 0.4281674 0.1028707 206.5081 0 0 mmu-miR-181a-5p mmu-miR-181a-5p 0.2454838 -0.4277786 -0.5874128 -0.6785543 -0.3003926 0.6785543
mmu-miR-15b-5p 444.43295 0.1121891 0.1380392 204.4162 0 0 mmu-miR-15b-5p mmu-miR-15b-5p 0.5679018 0.4463544 0.9437916 1.3881331 1.2517012 1.3881331
mmu-let-7j 3660.48447 0.7473125 0.1423042 191.9095 0 0 mmu-let-7j mmu-let-7j 1.4174612 0.2660367 0.5022776 1.0472989 1.5061737 1.5061737
mmu-miR-18a-5p 140.48080 0.2058626 0.2645325 189.7141 0 0 mmu-miR-18a-5p mmu-miR-18a-5p 1.0554008 1.6065388 2.2486953 2.4126605 2.0513531 2.4126605
mmu-miR-298-5p 63.44882 1.9510187 0.4342741 189.7205 0 0 mmu-miR-298-5p mmu-miR-298-5p 3.0350291 1.0633386 3.1482262 4.0225210 3.8880722 4.0225210
mmu-miR-132-3p 995.91587 1.0972911 0.2259190 183.9388 0 0 mmu-miR-132-3p mmu-miR-132-3p 2.3521595 2.3004948 2.4792479 2.5213285 2.6462390 2.6462390
mmu-let-7i-5p 28002.55133 0.6010586 0.1546107 178.5984 0 0 mmu-let-7i-5p mmu-let-7i-5p 1.3966011 0.9385165 1.4164715 1.6881308 1.7028061 1.7028061
mmu-miR-221-3p 5762.96983 -0.0016366 0.0879878 167.2031 0 0 mmu-miR-221-3p mmu-miR-221-3p 0.2406472 0.8688996 0.6301371 0.5365345 0.3439440 0.8688996
mmu-miR-214-3p 671.15060 1.1800627 0.2515532 163.6752 0 0 mmu-miR-214-3p mmu-miR-214-3p 2.2324322 0.5469246 1.6893764 2.4875904 2.4306447 2.4875904
mmu-miR-223-3p 588.88017 1.1182354 0.2385065 160.3819 0 0 mmu-miR-223-3p mmu-miR-223-3p 1.7473818 1.8978195 2.2931158 2.5513986 2.6457617 2.6457617


Working with  119  genes 

Analysis for novel miRNA

## Comparison: fa_model_novel {.tabset}


out of 255 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 35, 14%
LFC < 0 (down) : 21, 8.2%
outliers [1] : 3, 1.2%
low counts [2] : 45, 18%
(mean count < 1)
[1] see ‘cooksCutoff’ argument of ?results
[2] see ‘independentFiltering’ argument of ?results

NULL

Differential expression file at: fa_model_novel.tsv

Normalized counts matrix file at: fa_model_novel_log2_counts.tsv

MA plot plot

Volcano plot

QC for DE genes p-values/variance

Most significand, FDR< 0.05 and log2FC > 1 : 28

Plots most significand

Plot top 9 genes

Top DE genes

baseMean log2FoldChange lfcSE stat pvalue padj symbol description Day14vsDay0 Day1vsDay0 Day2vsDay0 Day3vsDay0 Day7vsDay0 absMaxLog2FC
mmu-chr2_1617-5p 15704.04826 1.2477818 0.1746373 209.52844 0e+00 0.0e+00 mmu-chr2_1617-5p mmu-chr2_1617-5p 1.7027375 0.0082195 0.0858072 0.5345992 1.4062735 1.7027375
mmu-chr2_1617-3p 1604.50416 1.2688125 0.1741413 202.95721 0e+00 0.0e+00 mmu-chr2_1617-3p mmu-chr2_1617-3p 1.8256632 0.2348821 0.4584559 0.9290956 1.7115133 1.8256632
mmu-chr19_17788-5p 1870.89106 1.7255196 0.2440247 183.76794 0e+00 0.0e+00 mmu-chr19_17788-5p mmu-chr19_17788-5p 2.8532472 1.1836523 1.4959106 2.5986829 2.7055385 2.8532472
mmu-chr10_11097-5p 28102.83066 0.6957317 0.1572774 158.46900 0e+00 0.0e+00 mmu-chr10_11097-5p mmu-chr10_11097-5p 1.4408854 0.9261011 1.3499184 1.6070990 1.6992468 1.6992468
mmu-chr1_555-5p 667.46643 1.2691971 0.2622320 146.04206 0e+00 0.0e+00 mmu-chr1_555-5p mmu-chr1_555-5p 2.2758244 0.5344898 1.6321099 2.4187867 2.4222843 2.4222843
mmu-chr13_13968-5p 61.65890 -0.3566850 0.2542023 138.44277 0e+00 0.0e+00 mmu-chr13_13968-5p mmu-chr13_13968-5p -0.0269858 1.1506837 1.5581120 0.8687753 0.9284907 1.5581120
mmu-chr1_555-3p 115.13914 1.3546718 0.2391823 116.86146 0e+00 0.0e+00 mmu-chr1_555-3p mmu-chr1_555-3p 1.8908435 0.2770808 1.0133671 1.4400643 1.8729387 1.8908435
mmu-chr11_11842-5p 1382.93118 0.9296450 0.2507180 98.17101 0e+00 0.0e+00 mmu-chr11_11842-5p mmu-chr11_11842-5p 1.0489480 -0.6201143 -0.8844405 -0.0090860 0.3996378 1.0489480
mmu-chr4_4731-5p 639.48337 1.5006912 0.2233972 86.16079 0e+00 0.0e+00 mmu-chr4_4731-5p mmu-chr4_4731-5p 1.7109593 1.5824969 1.8862033 1.8008005 1.9147657 1.9147657
mmu-chr7_7254-3p 62.05451 0.7366221 0.2251416 84.25959 0e+00 0.0e+00 mmu-chr7_7254-3p mmu-chr7_7254-3p 0.7659992 0.9640480 1.2852468 1.6636096 1.0832762 1.6636096
mmu-chr11_11562-5p 7094.45722 0.2026164 0.0778748 83.73320 0e+00 0.0e+00 mmu-chr11_11562-5p mmu-chr11_11562-5p 0.2720386 -0.1950919 -0.2988935 -0.1130372 -0.0034623 0.2988935
mmu-chrX_18627-3p 777.30582 -0.0683547 0.1333196 71.97971 0e+00 0.0e+00 mmu-chrX_18627-3p mmu-chrX_18627-3p -0.4616503 -0.6040915 -0.6853519 -0.8646916 -0.6407676 0.8646916
mmu-chr4_4731-3p 11.64074 5.5864065 1.2446180 71.53433 0e+00 0.0e+00 mmu-chr4_4731-3p mmu-chr4_4731-3p 5.9332551 5.8722774 6.1835144 5.3788624 6.9622301 6.9622301
mmu-chr14_14324-5p 650.20373 0.1321476 0.1342842 69.72374 0e+00 0.0e+00 mmu-chr14_14324-5p mmu-chr14_14324-5p 0.2733892 0.8354807 0.6416453 0.5808258 0.7394271 0.8354807
mmu-chr12_13064-5p 242.72311 -0.2393021 0.2138295 61.63439 0e+00 0.0e+00 mmu-chr12_13064-5p mmu-chr12_13064-5p -0.7873948 -0.1124744 -0.6147547 -0.8729946 -1.4391207 1.4391207
mmu-chr7_7254-5p 352.21891 0.1637386 0.1375269 59.25820 0e+00 0.0e+00 mmu-chr7_7254-5p mmu-chr7_7254-5p 0.3285514 0.1666189 0.5267945 0.7043941 0.7707181 0.7707181
mmu-chr8_8358-5p 11893.87663 -0.6576385 0.5002037 55.69714 0e+00 0.0e+00 mmu-chr8_8358-5p mmu-chr8_8358-5p -0.8809473 1.1651061 1.5080060 -1.0549920 -0.7001651 1.5080060
mmu-chr11_11568-3p 605.58914 0.3205571 0.1255872 46.53153 0e+00 3.0e-07 mmu-chr11_11568-3p mmu-chr11_11568-3p 0.3391268 -0.1641964 -0.1565013 -0.2148585 0.2693321 0.3391268
mmu-chr18_17508-5p 371.97170 -0.3588681 0.1276606 42.78688 1e-07 1.4e-06 mmu-chr18_17508-5p mmu-chr18_17508-5p -0.6669813 -0.2609442 -0.4787330 -0.5794412 -0.6441856 0.6669813
mmu-chr8_9013-5p 139.09155 -0.1207145 0.2029023 40.54146 4e-07 3.7e-06 mmu-chr8_9013-5p mmu-chr8_9013-5p -1.1053441 -0.0885767 -0.1244822 0.0506256 -0.5256979 1.1053441


Working with  28  genes 

Analysis for isomiRs

## Comparison: fa_model_isomir {.tabset}


out of 9976 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 1140, 11%
LFC < 0 (down) : 906, 9.1%
outliers [1] : 1, 0.01%
low counts [2] : 3095, 31%
(mean count < 1)
[1] see ‘cooksCutoff’ argument of ?results
[2] see ‘independentFiltering’ argument of ?results

NULL

Differential expression file at: fa_model_isomir.tsv

Normalized counts matrix file at: fa_model_isomir_log2_counts.tsv

MA plot plot

Volcano plot

QC for DE genes p-values/variance

Most significand, FDR< 0.05 and log2FC > 1 : 1071

Plots most significand

Plot top 9 genes

Top DE genes

baseMean log2FoldChange lfcSE stat pvalue padj symbol description Day14vsDay0 Day1vsDay0 Day2vsDay0 Day3vsDay0 Day7vsDay0 absMaxLog2FC
mmu-miR-21a-5p.iso.t5:0.t3:C.ad:C.mm:0 11278.35779 0.2023678 0.2104789 544.8132 0 0 mmu-miR-21a-5p.iso.t5:0.t3:C.ad:C.mm:0 mmu-miR-21a-5p.iso.t5:0.t3:C.ad:C.mm:0 1.1912110 3.5583135 3.6269413 3.1720696 2.5279801 3.6269413
mmu-miR-21a-5p.iso.t5:A.t3:0.ad:0.mm:0 846.80622 0.1694778 0.1776450 531.5104 0 0 mmu-miR-21a-5p.iso.t5:A.t3:0.ad:0.mm:0 mmu-miR-21a-5p.iso.t5:A.t3:0.ad:0.mm:0 1.3519726 2.1691581 2.8615896 2.8224250 2.4868270 2.8615896
mmu-miR-21a-5p.iso.t5:0.t3:C.ad:A.mm:0 5274.01122 -0.0478811 0.1617310 515.1055 0 0 mmu-miR-21a-5p.iso.t5:0.t3:C.ad:A.mm:0 mmu-miR-21a-5p.iso.t5:0.t3:C.ad:A.mm:0 0.9437825 1.9337577 2.6396474 2.4719199 2.0068352 2.6396474
mmu-miR-21a-5p.iso.t5:A.t3:C.ad:0.mm:0 58.20176 -0.3647279 0.4794563 467.9483 0 0 mmu-miR-21a-5p.iso.t5:A.t3:C.ad:0.mm:0 mmu-miR-21a-5p.iso.t5:A.t3:C.ad:0.mm:0 1.4586001 3.6699715 4.0272885 3.7082023 2.9310927 4.0272885
mmu-miR-93-5p.ref.t5:0.t3:0.ad:0.mm:0 1410.00405 0.1081940 0.0882399 444.6899 0 0 mmu-miR-93-5p.ref.t5:0.t3:0.ad:0.mm:0 mmu-miR-93-5p.ref.t5:0.t3:0.ad:0.mm:0 0.4904528 0.6303569 1.1226739 1.3333864 1.1243616 1.3333864
mmu-miR-21a-5p.iso.t5:0.t3:CT.ad:0.mm:0 64309.70315 0.2910704 0.1757690 444.2239 0 0 mmu-miR-21a-5p.iso.t5:0.t3:CT.ad:0.mm:0 mmu-miR-21a-5p.iso.t5:0.t3:CT.ad:0.mm:0 1.3727513 2.4099209 2.7935148 2.7337894 2.4797689 2.7935148
mmu-miR-146b-5p.iso.t5:0.t3:G.ad:0.mm:0 542.44226 1.6358763 0.1742347 414.0365 0 0 mmu-miR-146b-5p.iso.t5:0.t3:G.ad:0.mm:0 mmu-miR-146b-5p.iso.t5:0.t3:G.ad:0.mm:0 2.6926429 0.5428686 1.1670029 2.3111355 2.5424858 2.6926429
mmu-miR-199a-3p.iso.t5:0.t3:0.ad:A.mm:0 620.98234 1.5142002 0.1553404 397.0422 0 0 mmu-miR-199a-3p.iso.t5:0.t3:0.ad:A.mm:0 mmu-miR-199a-3p.iso.t5:0.t3:0.ad:A.mm:0 2.1517905 0.1785731 0.2175869 0.7617868 1.8248843 2.1517905
mmu-miR-199b-3p.iso.t5:0.t3:0.ad:A.mm:0 620.98234 1.5142002 0.1553404 397.0422 0 0 mmu-miR-199b-3p.iso.t5:0.t3:0.ad:A.mm:0 mmu-miR-199b-3p.iso.t5:0.t3:0.ad:A.mm:0 2.1517905 0.1785731 0.2175869 0.7617868 1.8248843 2.1517905
mmu-miR-21a-3p.iso.t5:c.t3:T.ad:0.mm:0 423.66178 0.0801993 0.2469328 377.9940 0 0 mmu-miR-21a-3p.iso.t5:c.t3:T.ad:0.mm:0 mmu-miR-21a-3p.iso.t5:c.t3:T.ad:0.mm:0 1.0153445 2.7914134 3.3196679 2.8470052 2.2915384 3.3196679
mmu-miR-21a-5p.iso.t5:0.t3:C.ad:0.mm:0 65864.48117 0.2671976 0.2322570 368.7898 0 0 mmu-miR-21a-5p.iso.t5:0.t3:C.ad:0.mm:0 mmu-miR-21a-5p.iso.t5:0.t3:C.ad:0.mm:0 1.5005346 3.0306639 3.4940082 3.1911338 2.6853264 3.4940082
mmu-miR-21a-3p.ref.t5:0.t3:c.ad:0.mm:0 1870.15869 0.1013924 0.2392049 357.4214 0 0 mmu-miR-21a-3p.ref.t5:0.t3:c.ad:0.mm:0 mmu-miR-21a-3p.ref.t5:0.t3:c.ad:0.mm:0 1.0332505 2.7361909 3.3841446 2.9207550 2.2230688 3.3841446
mmu-miR-92a-1-5p.ref.t5:0.t3:0.ad:0.mm:0 135.00995 -0.1503253 0.1701043 345.2868 0 0 mmu-miR-92a-1-5p.ref.t5:0.t3:0.ad:0.mm:0 mmu-miR-92a-1-5p.ref.t5:0.t3:0.ad:0.mm:0 0.4095784 1.4553068 1.7380425 1.4271687 0.9668157 1.7380425
mmu-miR-181c-5p.iso.t5:0.t3:T.ad:0.mm:0 1523.53821 0.8405588 0.1209044 338.0922 0 0 mmu-miR-181c-5p.iso.t5:0.t3:T.ad:0.mm:0 mmu-miR-181c-5p.iso.t5:0.t3:T.ad:0.mm:0 1.0765788 -0.4191428 -0.3580316 -0.2513222 0.7990521 1.0765788
mmu-miR-181a-5p.iso.t5:0.t3:T.ad:0.mm:0 1668.80801 0.3241937 0.1003742 331.6893 0 0 mmu-miR-181a-5p.iso.t5:0.t3:T.ad:0.mm:0 mmu-miR-181a-5p.iso.t5:0.t3:T.ad:0.mm:0 0.6717940 -0.6926857 -0.5537966 0.2590926 0.6694544 0.6926857
mmu-miR-21a-5p.iso.t5:0.t3:C.ad:G.mm:0 296.58796 0.2817179 0.2277302 317.3796 0 0 mmu-miR-21a-5p.iso.t5:0.t3:C.ad:G.mm:0 mmu-miR-21a-5p.iso.t5:0.t3:C.ad:G.mm:0 1.5396964 2.3395675 2.7828531 2.7714881 2.2489409 2.7828531
mmu-miR-21a-5p.iso.t5:A.t3:CT.ad:0.mm:0 76.52812 0.9297556 0.3853488 317.1316 0 0 mmu-miR-21a-5p.iso.t5:A.t3:CT.ad:0.mm:0 mmu-miR-21a-5p.iso.t5:A.t3:CT.ad:0.mm:0 1.9237260 3.5530122 3.8607423 3.7776356 3.2155839 3.8607423
mmu-miR-21a-5p.ref.t5:0.t3:0.ad:0.mm:0 74058.19837 0.7161811 0.1692574 311.4091 0 0 mmu-miR-21a-5p.ref.t5:0.t3:0.ad:0.mm:0 mmu-miR-21a-5p.ref.t5:0.t3:0.ad:0.mm:0 1.7579095 0.6885654 1.7312604 2.0926706 2.4943871 2.4943871
mmu-miR-92a-3p.iso.t5:0.t3:T.ad:0.mm:0 25826.04849 0.0024922 0.1148568 307.8952 0 0 mmu-miR-92a-3p.iso.t5:0.t3:T.ad:0.mm:0 mmu-miR-92a-3p.iso.t5:0.t3:T.ad:0.mm:0 0.1266860 1.1327519 1.3676340 1.0886733 0.6160221 1.3676340
mmu-miR-142a-5p.iso.t5:CC.t3:t.ad:0.mm:0 606.16226 1.3471400 0.1817580 303.3488 0 0 mmu-miR-142a-5p.iso.t5:CC.t3:t.ad:0.mm:0 mmu-miR-142a-5p.iso.t5:CC.t3:t.ad:0.mm:0 2.1017335 1.1788563 1.8938661 2.7462601 2.5578597 2.7462601


Working with  1071  genes 

Analysis for clusters

## Comparison: fa_model_cluster {.tabset}


out of 587 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 157, 27%
LFC < 0 (down) : 181, 31%
outliers [1] : 2, 0.34%
low counts [2] : 0, 0%
(mean count < 6)
[1] see ‘cooksCutoff’ argument of ?results
[2] see ‘independentFiltering’ argument of ?results

NULL

Differential expression file at: fa_model_cluster.tsv

Normalized counts matrix file at: fa_model_cluster_log2_counts.tsv

MA plot plot

Volcano plot

QC for DE genes p-values/variance

Most significand, FDR< 0.05 and log2FC > 1 : 178

Plots most significand

Plot top 9 genes

Top DE genes

baseMean log2FoldChange lfcSE stat pvalue padj symbol description Day14vsDay0 Day1vsDay0 Day2vsDay0 Day3vsDay0 Day7vsDay0 absMaxLog2FC
433 2249.48322 0.9070674 0.3732575 222.37775 0 0 433 433 1.5714264 4.4169261 3.9961329 3.0406021 1.4994816 4.4169261
524 1825.59642 1.4928244 0.2247476 199.26349 0 0 524 524 2.5176476 0.7171127 1.3548461 2.3580632 2.4769055 2.5176476
581 830.92108 0.0551098 0.1808680 187.59921 0 0 581 581 0.3845358 1.6897462 1.5958847 0.9544170 0.6051035 1.6897462
10 248.67918 0.4301422 0.2060157 163.91234 0 0 10 10 1.2873803 1.4984656 1.6483184 1.7704845 2.0143328 2.0143328
143 61.38041 2.0475255 0.5029248 142.34243 0 0 143 143 3.0424560 0.7118289 3.1930821 3.9497840 3.9643826 3.9643826
343 64.55927 1.9278647 0.6809845 116.36999 0 0 343 343 1.9666072 5.1165617 4.7747046 3.7541310 2.1610405 5.1165617
209 91487.78417 -0.3005822 0.1714544 107.66715 0 0 209 209 -0.7211153 -1.0813466 -1.2883259 -1.2687378 -1.0533429 1.2883259
352 234955.42108 0.4043253 0.2683826 107.35545 0 0 352 352 1.2836260 1.5718652 2.1898987 2.0069401 2.0910592 2.1898987
17 158.85562 0.0399798 0.3281629 102.62231 0 0 17 17 0.7545601 1.2153195 2.1486236 2.1108204 1.8672980 2.1486236
450 135.84650 0.7362333 0.3167871 102.28819 0 0 450 450 1.5572398 0.2354885 1.3871195 2.1389756 2.2574049 2.2574049
376 213.56965 1.7275483 0.5997336 101.99441 0 0 376 376 2.0418641 4.4838385 4.7469158 4.7586558 2.7714175 4.7586558
12 272.48124 -0.7432530 0.3095413 101.31684 0 0 12 12 -1.7948377 -1.2281486 -1.6316536 -2.0856325 -2.7247396 2.7247396
11 7222.51936 -0.3761254 0.2352652 90.01777 0 0 11 11 -1.2622680 -0.5538625 -0.8623613 -1.3182918 -1.9443384 1.9443384
323 203.10656 0.0512942 0.3011742 88.51524 0 0 323 323 0.3118882 1.5420878 1.7928676 1.6646676 1.4653415 1.7928676
576 64.89637 0.2575567 0.5076091 88.65939 0 0 576 576 0.3177388 2.9555556 2.6581699 1.6229509 0.5102446 2.9555556
182 1348.14078 0.6991956 0.2707282 82.40146 0 0 182 182 0.6959874 -1.0719157 -1.0462163 -0.2446071 0.2105213 1.0719157
387 133.43965 0.6631465 0.2871488 80.42204 0 0 387 387 1.6993664 1.6947198 1.3250564 1.9442881 1.9834675 1.9834675
142 5353.05737 0.0412670 0.1113550 79.01163 0 0 142 142 0.0445159 -0.5129584 -0.5877018 -0.4413173 -0.0035560 0.5877018
13 776.45634 1.0674236 0.3361205 78.34878 0 0 13 13 1.9435945 0.1130401 1.4074679 2.0754668 2.1628673 2.1628673
320 165.18074 1.0760742 0.4182942 78.22436 0 0 320 320 2.1496170 2.4107670 2.9185996 2.9433578 2.9779445 2.9779445


Working with  178  genes 

Files

Files generated contains raw count, normalized counts, log2 normalized counts and DESeq2 results.

R Session Info

(useful if replicating these results)

R version 3.3.0 (2016-05-03)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux stretch/sid

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
 [1] grid      parallel  stats4    methods   stats     graphics  grDevices
 [8] utils     datasets  base     

other attached packages:
 [1] vsn_3.40.0                 edgeR_3.14.0              
 [3] limma_3.28.4               DEGreport_1.5.0           
 [5] quantreg_5.24              SparseM_1.7               
 [7] org.Mm.eg.db_3.3.0         AnnotationDbi_1.34.2      
 [9] myRfunctions_0.1           cluster_2.0.4             
[11] pheatmap_1.0.8             isomiRs_0.99.13           
[13] DiscriMiner_0.1-29         dplyr_0.4.3               
[15] devtools_1.11.1            gridExtra_2.2.1           
[17] gtools_3.5.0               CHBUtils_0.1              
[19] genefilter_1.54.2          DESeq2_1.12.2             
[21] SummarizedExperiment_1.2.2 Biobase_2.32.0            
[23] GenomicRanges_1.24.0       GenomeInfoDb_1.8.1        
[25] IRanges_2.6.0              S4Vectors_0.10.0          
[27] BiocGenerics_0.18.0        reshape_0.8.5             
[29] ggplot2_2.1.0              knitr_1.13                
[31] rmarkdown_0.9.6            BiocInstaller_1.22.2      

loaded via a namespace (and not attached):
 [1] bitops_1.0-6          RColorBrewer_1.1-2    tools_3.3.0          
 [4] R6_2.1.2              affyio_1.42.0         rpart_4.1-10         
 [7] KernSmooth_2.23-15    Hmisc_3.17-4          DBI_0.4-1            
[10] lazyeval_0.1.10       colorspace_1.2-6      nnet_7.3-12          
[13] withr_1.0.1           GGally_1.0.1          Nozzle.R1_1.1-1      
[16] preprocessCore_1.34.0 chron_2.3-47          formatR_1.4          
[19] logging_0.7-103       labeling_0.3          caTools_1.17.1       
[22] scales_0.4.0          affy_1.50.0           stringr_1.0.0        
[25] digest_0.6.9          foreign_0.8-66        XVector_0.12.0       
[28] htmltools_0.3.5       highr_0.6             RSQLite_1.0.0        
[31] BiocParallel_1.6.2    acepack_1.3-3.3       magrittr_1.5         
[34] Formula_1.2-1         Matrix_1.2-6          Rcpp_0.12.5          
[37] munsell_0.4.3         stringi_1.0-1         yaml_2.1.13          
[40] zlibbioc_1.18.0       gplots_3.0.1          plyr_1.8.3           
[43] gdata_2.17.0          lattice_0.20-33       splines_3.3.0        
[46] annotate_1.50.0       locfit_1.5-9.1        geneplotter_1.50.0   
[49] codetools_0.2-14      XML_3.98-1.4          evaluate_0.9         
[52] latticeExtra_0.6-28   data.table_1.9.6      MatrixModels_0.4-1   
[55] gtable_0.2.0          tidyr_0.4.1           assertthat_0.1       
[58] xtable_1.8-2          coda_0.18-1           survival_2.39-4      
[61] memoise_1.0.0